Content recommendation based upon continuity and grouping information of attributes

ABSTRACT

One or more computing devices, systems, and/or methods for content recommendation based upon continuity and grouping information of attributes are provided herein. User interaction data specifying whether users interacted with content items, user attributes of the users, and content attributes of the content items is obtained. A data structure is populated with the user interaction data. The data structure is modified by inserting a set of sub-fields into the data structure for a user attribute. A sub-field is populated with a value representing an option of the user attribute. The set of sub-fields are an encoding of continuity information and grouping information representing options for the user attribute. The data structure is processed using machine learning functionality to generate a model. The model is utilized to generate a prediction as to whether a user will interact with a content item.

RELATED APPLICATION

This application claims priority to and is a continuation of U.S.Application No. 16/800,504, filed on Feb. 25, 2020, entitled “CONTENTRECOMMENDATION BASED UPON CONTINUITY AND GROUPING INFORMATION OFATTRIBUTES”, which is incorporated by reference herein in its entirety.

BACKGROUND

A content recommendation system may be configured to provide contentitems to users. For example, a content item may be provided to a clientcomputing device of a user, such as a mobile device (e.g., an imagedisplayed on a webpage accessed by a browser of the mobile device), awearable device (e.g., a push notification through a smart watch), asmart device (e.g., a recommendation of a movie to watch through a videostreaming service accessed by a smart television), or other computingdevices. The content recommendation system may recommend various typesof content items, such as videos to watch, images to view, items topurchase (e.g., a recommendation to purchase a vehicle), services topurchase (e.g., a recommendation to have a furnace cleaned), arestaurant recommendation, songs to listen to, etc.

The content recommendation system may utilize machine learningfunctionality and/or a model to predict likelihoods that a particularuser will interact with content items. Once trained by the machinelearning functionality, the model outputs predictions of how likely theuser is to interact with content items based upon content attributes ofthe content items and user attributes of the user. Unfortunately,typical machine learning mechanisms which are used for contentrecommendation require that user attributes and content attributes mustbe limited to a discrete and finite set of attribute values. Despitethis limitation, these mechanisms tend to be effective. For example,time must be represented by a discrete finite set of values (e.g., 0, 1,2, ..., 23). The content recommendation system is limited toindividually taking into account individual attribute hours and how eachindividual attribute hour will affect the probability of the userinteracting with content items. As a consequence, such contentrecommendation systems cannot account for similar user behavior atnearby times (e.g., a user may have similar behavior at 2:00am and3:00am or at 7:00pm and 8:00pm, but the content recommendation systemcan only take into account the effects that a single isolated hour hason user behavior). This significantly reduces the precision and accuracyof outputting accurate predictions of how user attributes and contentattributes affect the probability of users interacting with contentitems.

SUMMARY

In accordance with the present disclosure, one or more computing devicesand/or methods for content recommendation based upon continuity andgrouping information of attributes are provided. Machine learningfunctionality may be utilized to train and generate a model based uponuser interaction data, such as past/historic user interaction data. Theuser interaction data may specify whether users interacted with contentitems (e.g., did a user watch a video provided as a suggestion by avideo streaming service or not, did a user click on a recommendation topurchase an item or not, did a user purchase a recommended service ornot, etc.). The user interaction data may specify user attributes of theusers that either did or did not interact with the content items afterbeing provided with an opportunity to interact with the content items.The user attributes may correspond to an age of a user, a location of auser, a time at which a user performed an action such as logging intothe video streaming service, a current location of the user, a homelocation of the user, demographic information about the user, interestsof the user (e.g., the user is interested in playing videogames), and/ora wide variety of information about the user, activities of the user,preferences of the user, etc. The user interaction data may specifycontent attributes of the content items that were identified as eitherbeing interacted with or not by particular users. The content attributesmay correspond to a content identifier, a content topic/category (e.g.,cars, romance movies, pop music, clothing for sale, sports, etc.), atype of content (e.g., movie, image, hyperlink to a website, audiomessage, text, etc.), when the content was generated, an author of thecontent, and/or a variety of other attributes of content items.

The machine learning functionality and/or the model, such as afactorization machine, may natively expect to receive attributes asdiscrete values of a finite set (e.g., age represented by discrete yearsof 1 years old, 2 years old, 3 years old, etc. of a finite set of agesfrom 1 year old to 101 years old). This does not take into accountcontinuity of values in the real world (e.g., time/age is a continuousconcept in the real world as opposed to discrete isolated individualvalues), and thus the model cannot understand and take into accountsimilarities in user behavior across multiple values such as where a 32year old may have similar behavior as a 33 year old. Accordingly, asprovided herein, continuity information and grouping information areencoded into data, such as a data structure, used to train and executethe model. The data structure, such as a table or any other structurecapable of storing data, may be populated with the user interactiondata. For a particular user attribute (e.g., ages of users), userattributes values of the user attribute are populated within a first setof fields (e.g., an age column populated with ages of users). For aparticular content attribute (e.g., categories of content items),content attribute values of the content attribute are populated within asecond set of fields (e.g., a category column populated with categoriesof content items). User interaction indicator values are populatedwithin a third set of fields (e.g., a third column of whether a userprovided with a content item interacted with the content item or did notinteract with the content item).

In order to encode the continuity information and the groupinginformation into the data structure, the data structure is modified byinserting a set of sub-fields into the data structure for an attribute(e.g., a set of columns for a user attribute or a content attribute).The set of sub-fields are an encoding of continuity information andgrouping information representing options (sub-categories) for theattribute. For example, a sub-field of an attribute value is populatedwith a value (e.g., a “1” or a “0”) indicating whether the attributevalue corresponds to an option (sub-category) of the attributerepresented by the sub-field. In an example, the set of sub-fields arepopulated with gray code information corresponding to options(sub-categories) of the attribute. For example, a first age value may berepresented by a gray code of 3 digits 000, a second age value may berepresented by a gray code of 3 digits 001, a third age value may berepresented by a gray code of 3 digits 011, etc. The first digitcorresponds to a first option of an age attribute, the second digitcorresponds to a second option of the age attribute, and the third digitcorresponds to a third option of the age attribute.

The gray code information may be monotonic (e.g., a last gray code valuecan significantly differ from a first gray code value) or cyclic (e.g.,a last gray code value differs from a first gray code value by merely 1digit). In an example, each consecutive gray code value differs by asingle digit (e.g., 000 differs from 001 by the third digit, 001 differsby 011 by the second digit, etc.). Each digit of the gray codeinformation adds additional information about an attribute value of anattribute such as whether the attribute value corresponds to an option(sub-category) of the attribute or not, and thus the gray codeinformation can be used to train to model to take into accountcontinuity and groupings of similar attribute values (e.g., attributevalues that correspond to similar options). Because of the continuity ofgray codes (e.g., each consecutive number differs only in 1 digit), twoconsecutive gray codes may correspond to almost the same options(sub-categories) except for one option (the 1 differing digit) thatdifferentiates them. This information may be used by the model tounderstand and correlate together user behavior of users that havesimilar attributes (e.g., users that are 32 years old and 33 years oldmay behave similarly). In this way, the model can learn an explicitrepresentation which contains information about interactions of usersand items that are grouped together (e.g., each consecutive gray codewill merely differ by a single digit, and other digits will be the sameand thus part of the same/similar groups/options).

After encoding of the continuity information and grouping information,the data structure is processed using machine learning functionality togenerate a model. The model is significantly more accurate because it isadditionally trained to take into account multiple options (subgroups)for a single attribute value based upon the continuity information andgrouping information (e.g., instead of merely accounting for a singlevalue of age 30, the model takes into account additional options encodedby using multiple digits of gray code information such as 3 digits of a3 digit grey code used to represent different options of age that theage of 30 could correspond to or not). In this way, the model may beutilized to more accurately and precisely predict likelihoods of whethera user will interaction with various content items. Accordingly, contentitems can be ranked based upon how likely the user will interact witheach content item, and a particular content item (e.g., a highest rankedcontent item) can be recommended and/or provided to the user, such astransmitted over a network to a client computing device for display tothe user.

DESCRIPTION OF THE DRAWINGS

While the techniques presented herein may be embodied in alternativeforms, the particular embodiments illustrated in the drawings are only afew examples that are supplemental of the description provided herein.These embodiments are not to be interpreted in a limiting manner, suchas limiting the claims appended hereto.

FIG. 1 is an illustration of a scenario involving various examples ofnetworks that may connect servers and clients.

FIG. 2 is an illustration of a scenario involving an exampleconfiguration of a server that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 3 is an illustration of a scenario involving an exampleconfiguration of a client that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 4 is a flow chart illustrating an example method for contentrecommendation based upon continuity and grouping information ofattributes.

FIG. 5 is an illustration of a visual representation of a cyclic graycode.

FIG. 6 is a component block diagram illustrating an example system forcontent recommendation based upon continuity and grouping information ofattributes, where a set of sub-fields are inserted into a datastructure.

FIG. 7 is a component block diagram illustrating an example system forcontent recommendation based upon continuity and grouping information ofattributes, where a model is generated.

FIG. 8 is a component block diagram illustrating an example system forcontent recommendation based upon continuity and grouping information ofattributes, where a model is used to generate predictions as to whethera user will interact with content items.

FIG. 9 is a component block diagram illustrating an example system forcontent recommendation based upon continuity and grouping information ofattributes, where a model is used to generate predictions as to whethera user will interact with content items.

FIG. 10 is an illustration of a scenario featuring an examplenon-transitory machine readable medium in accordance with one or more ofthe provisions set forth herein.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments. Thisdescription is not intended as an extensive or detailed discussion ofknown concepts. Details that are known generally to those of ordinaryskill in the relevant art may have been omitted, or may be handled insummary fashion.

The following subject matter may be embodied in a variety of differentforms, such as methods, devices, components, and/or systems.Accordingly, this subject matter is not intended to be construed aslimited to any example embodiments set forth herein. Rather, exampleembodiments are provided merely to be illustrative. Such embodimentsmay, for example, take the form of hardware, software, firmware or anycombination thereof.

1. Computing Scenario

The following provides a discussion of some types of computing scenariosin which the disclosed subject matter may be utilized and/orimplemented.

1.1. Networking

FIG. 1 is an interaction diagram of a scenario 100 illustrating aservice 102 provided by a set of servers 104 to a set of client devices110 via various types of networks. The servers 104 and/or client devices110 may be capable of transmitting, receiving, processing, and/orstoring many types of signals, such as in memory as physical memorystates.

The servers 104 of the service 102 may be internally connected via alocal area network 106 (LAN), such as a wired network where networkadapters on the respective servers 104 are interconnected via cables(e.g., coaxial and/or fiber optic cabling), and may be connected invarious topologies (e.g., buses, token rings, meshes, and/or trees). Theservers 104 may be interconnected directly, or through one or more othernetworking devices, such as routers, switches, and/or repeaters. Theservers 104 may utilize a variety of physical networking protocols(e.g., Ethernet and/or Fiber Channel) and/or logical networkingprotocols (e.g., variants of an Internet Protocol (lP), a TransmissionControl Protocol (TCP), and/or a User Datagram Protocol (UDP). The localarea network 106 may include, e.g., analog telephone lines, such as atwisted wire pair, a coaxial cable, full or fractional digital linesincluding T1, T2, T3, or T4 type lines, Integrated Services DigitalNetworks (ISDNs), Digital Subscriber Lines (DSLs), wireless linksincluding satellite links, or other communication links or channels,such as may be known to those skilled in the art. The local area network106 may be organized according to one or more network architectures,such as server/client, peer-to-peer, and/or mesh architectures, and/or avariety of roles, such as administrative servers, authenticationservers, security monitor servers, data stores for objects such as filesand databases, business logic servers, time synchronization servers,and/or front-end servers providing a user-facing interface for theservice 102.

Likewise, the local area network 106 may comprise one or moresub-networks, such as may employ differing architectures, may becompliant or compatible with differing protocols and/or may interoperatewithin the local area network 106. Additionally, a variety of local areanetworks 106 may be interconnected; e.g., a router may provide a linkbetween otherwise separate and independent local area networks 106.

In the scenario 100 of FIG. 1 , the local area network 106 of theservice 102 is connected to a wide area network 108 (WAN) that allowsthe service 102 to exchange data with other services 102 and/or clientdevices 110. The wide area network 108 may encompass variouscombinations of devices with varying levels of distribution andexposure, such as a public wide-area network (e.g., the Internet) and/ora private network (e.g., a virtual private network (VPN) of adistributed enterprise).

In the scenario 100 of FIG. 1 , the service 102 may be accessed via thewide area network 108 by a user 112 of one or more client devices 110,such as a portable media player (e.g., an electronic text reader, anaudio device, or a portable gaming, exercise, or navigation device); aportable communication device (e.g., a camera, a phone, a wearable or atext chatting device); a workstation; and/or a laptop form factorcomputer. The respective client devices 110 may communicate with theservice 102 via various connections to the wide area network 108. As afirst such example, one or more client devices 110 may comprise acellular communicator and may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a cellular provider. As a second such example,one or more client devices 110 may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a location such as the user’s home or workplace(e.g., a WiFi (Institute of Electrical and Electronics Engineers (IEEE)Standard 802.11) network or a Bluetooth (IEEE Standard 802.15.1)personal area network). In this manner, the servers 104 and the clientdevices 110 may communicate over various types of networks. Other typesof networks that may be accessed by the servers 104 and/or clientdevices 110 include mass storage, such as network attached storage(NAS), a storage area network (SAN), or other forms of computer ormachine readable media.

1.2. Server Configuration

FIG. 2 presents a schematic architecture diagram 200 of a server 104that may utilize at least a portion of the techniques provided herein.Such a server 104 may vary widely in configuration or capabilities,alone or in conjunction with other servers, in order to provide aservice such as the service 102.

The server 104 may comprise one or more processors 210 that processinstructions. The one or more processors 210 may optionally include aplurality of cores; one or more coprocessors, such as a mathematicscoprocessor or an integrated graphical processing unit (GPU); and/or oneor more layers of local cache memory. The server 104 may comprise memory202 storing various forms of applications, such as an operating system204; one or more server applications 206, such as a hypertext transportprotocol (HTTP) server, a file transfer protocol (FTP) server, or asimple mail transport protocol (SMTP) server; and/or various forms ofdata, such as a database 208 or a file system. The server 104 maycomprise a variety of peripheral components, such as a wired and/orwireless network adapter 214 connectible to a local area network and/orwide area network; one or more storage components 216, such as a harddisk drive, a solid-state storage device (SSD), a flash memory device,and/or a magnetic and/or optical disk reader.

The server 104 may comprise a mainboard featuring one or morecommunication buses 212 that interconnect the processor 210, the memory202, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol; aUniform Serial Bus (USB) protocol; and/or Small Computer SystemInterface (SCI) bus protocol. In a multibus scenario, a communicationbus 212 may interconnect the server 104 with at least one other server.Other components that may optionally be included with the server 104(though not shown in the schematic architecture diagram 200 of FIG. 2 )include a display; a display adapter, such as a graphical processingunit (GPU); input peripherals, such as a keyboard and/or mouse; and aflash memory device that may store a basic input/output system (BIOS)routine that facilitates booting the server 104 to a state of readiness.

The server 104 may operate in various physical enclosures, such as adesktop or tower, and/or may be integrated with a display as an“all-in-one” device. The server 104 may be mounted horizontally and/orin a cabinet or rack, and/or may simply comprise an interconnected setof components. The server 104 may comprise a dedicated and/or sharedpower supply 218 that supplies and/or regulates power for the othercomponents. The server 104 may provide power to and/or receive powerfrom another server and/or other devices. The server 104 may comprise ashared and/or dedicated climate control unit 220 that regulates climateproperties, such as temperature, humidity, and/or airflow. Many suchservers 104 may be configured and/or adapted to utilize at least aportion of the techniques presented herein.

1.3. Client Device Configuration

FIG. 3 presents a schematic architecture diagram 300 of a client device110 whereupon at least a portion of the techniques presented herein maybe implemented. Such a client device 110 may vary widely inconfiguration or capabilities, in order to provide a variety offunctionality to a user such as the user 112. The client device 110 maybe provided in a variety of form factors, such as a desktop or towerworkstation; an “all-in-one” device integrated with a display 308; alaptop, tablet, convertible tablet, or palmtop device; a wearable devicemountable in a headset, eyeglass, earpiece, and/or wristwatch, and/orintegrated with an article of clothing; and/or a component of a piece offurniture, such as a tabletop, and/or of another device, such as avehicle or residence. The client device 110 may serve the user in avariety of roles, such as a workstation, kiosk, media player, gamingdevice, and/or appliance.

The client device 110 may comprise one or more processors 310 thatprocess instructions. The one or more processors 310 may optionallyinclude a plurality of cores; one or more coprocessors, such as amathematics coprocessor or an integrated graphical processing unit(GPU); and/or one or more layers of local cache memory. The clientdevice 110 may comprise memory 301 storing various forms ofapplications, such as an operating system 303; one or more userapplications 30 2, such as document applications, media applications,file and/or data access applications, communication applications such asweb browsers and/or email clients, utilities, and/or games; and/ordrivers for various peripherals. The client device 110 may comprise avariety of peripheral components, such as a wired and/or wirelessnetwork adapter 306 connectible to a local area network and/or wide areanetwork; one or more output components, such as a display 308 coupledwith a display adapter (optionally including a graphical processing unit(GPU)), a sound adapter coupled with a speaker, and/or a printer; inputdevices for receiving input from the user, such as a keyboard 311, amouse, a microphone, a camera, and/or a touch-sensitive component of thedisplay 308; and/or environmental sensors, such as a global positioningsystem (GPS) receiver 319 that detects the location, velocity, and/oracceleration of the client device 110, a compass, accelerometer, and/orgyroscope that detects a physical orientation of the client device 110.Other components that may optionally be included with the client device110 (though not shown in the schematic architecture diagram 300 of FIG.3 ) include one or more storage components, such as a hard disk drive, asolid-state storage device (SSD), a flash memory device, and/or amagnetic and/or optical disk reader; and/or a flash memory device thatmay store a basic input/output system (BIOS) routine that facilitatesbooting the client device 110 to a state of readiness; and a climatecontrol unit that regulates climate properties, such as temperature,humidity, and airflow.

The client device 110 may comprise a mainboard featuring one or morecommunication buses 312 that interconnect the processor 310, the memory301, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol;the Uniform Serial Bus (USB) protocol; and/or the Small Computer SystemInterface (SCI) bus protocol. The client device 110 may comprise adedicated and/or shared power supply 318 that supplies and/or regulatespower for other components, and/or a battery 304 that stores power foruse while the client device 110 is not connected to a power source viathe power supply 318. The client device 110 may provide power to and/orreceive power from other client devices.

In some scenarios, as a user 112 interacts with a software applicationon a client device 110 (e.g., an instant messenger and/or electronicmail application), descriptive content in the form of signals or storedphysical states within memory (e.g., an email address, instant messengeridentifier, phone number, postal address, message content, date, and/ortime) may be identified. Descriptive content may be stored, typicallyalong with contextual content. For example, the source of a phone number(e.g., a communication received from another user via an instantmessenger application) may be stored as contextual content associatedwith the phone number. Contextual content, therefore, may identifycircumstances surrounding receipt of a phone number (e.g., the date ortime that the phone number was received), and may be associated withdescriptive content. Contextual content, may, for example, be used tosubsequently search for associated descriptive content. For example, asearch for phone numbers received from specific individuals, receivedvia an instant messenger application or at a given date or time, may beinitiated. The client device 110 may include one or more servers thatmay locally serve the client device 110 and/or other client devices ofthe user 112 and/or other individuals. For example, a locally installedwebserver may provide web content in response to locally submitted webrequests. Many such client devices 110 may be configured and/or adaptedto utilize at least a portion of the techniques presented herein.

2. Presented Techniques

Techniques are provided for content recommendation based upon continuityand grouping information of attributes. A content recommendation systemmay be implemented as software and/or hardware by one or more computingdevices, such as a server. The content recommendation system may beconfigured to provide content items over a network to computing devicesconnected to the content recommendation system (e.g., a mobile device, acomputer, a wearable device, a smart device, or other client deviceconnected to the server over the network). A content item may comprisetext, an image, a movie, a song, a video, an article, a website, ahyperlink to a website, a description or recommendation of a physicalitem (e.g., a shirt, a car, an item for purchase, etc.) or service(e.g., a chiropractic service, an oil change service, a service forpurchase, etc.). A content item may be provided through variousapplications, services (e.g., a recommendation of a video availablethrough a video streaming service, a recommendation of a song availablethrough an audio streaming service, a recommendation of a videogame toplay through a videogame service, etc.), user interfaces, websites(e.g., an image or video displayed through a webpage), notifications(e.g., a push notification to a mobile device, a text message, an email,etc.), etc.

Different users have different interests and there may be hundreds ofthousands of different types of content items available to provide tousers. Accordingly, the content recommendation system may utilizemachine learning functionality and/or a model to predict how likelycertain users are to interact with certain content items. In this way,when an opportunity arises to provide a content item to a user (e.g., auser visits a website or accesses a user interface through which acontent item may be displayed), available content items may be ranked interms of how likely that user will interact with each content item. Ahighest ranked content item may then be provided to the user.

In order to output predictions of how likely the user is to interactwith content items, the content recommendation system executes the modelupon user attributes of the user and content attributes of the contentitems. The user attributes may correspond to an age of a user, interestsof a user, a time at which a user performed an action such as checkingemail in the morning, a current location of the user, a home location ofthe user, demographic information about the user, and/or a wide varietyof information about the user, activities of the user, preferences ofthe user, etc. The content attributes may correspond to a contentidentifier, a content topic/category (e.g., food, shopping, clothing,videogames, etc.), a type of content (e.g., movie, image, hyperlink to awebsite, etc.), and/or a variety of other attributes of content items.The model may process the user attributes and the content attributes inorder to output the predictions of how likely the user is to interactwith content items.

The model and/or the machine learning functionality of the contentrecommendation system, such as a factorization machine, may expectattributes to have discrete values within a finite set. In an example,instead of representing time as a continuous temporal representation astime exists in the real world, the model may expect and merely support arepresentation of time as a finite set of discrete hours from 0 to 23 ordiscrete groupings of multiple hours such as groupings of 3 hours wheregroup 1 represents hours 0 to 2:30am, group 2 represents hours 3:00am to5:59am, etc. In another example, instead of representing age as acontinuous age spectrum, the model may expect and merely support arepresentation of age as a finite set of discrete yearly ages, such as 1years old, 2 years old, 3 years old, etc. Accordingly, the model cannottake into account or interpret continuity of attribute values, such ashow time and age are continuous in the real world. Thus, the modelcannot take into account valuable information regarding similarity ofuser behavior outside the isolated individual discrete values of afinite set (e.g., the model cannot take into account the notion thatusers may behave similarly at ages 30 through 32). This inability totake into account additional information otherwise conveyed bycontinuity information and group information greatly reduces theaccuracy and precision of the model to accurately predict how likely theuser is to interact with content items. Inaccurate predictions willresult in the content recommendation system providing inaccuraterecommendations and content items to users that may ultimately ignore orbe annoyed by such inaccurate information, thus wasting networkbandwidth and processing resources used to identify and transmit contentitems over a network to client devices of users that may ultimatelyignore the content items.

Accordingly, as provided herein, continuity information and groupinginformation is used to train the model and is encoded within the modelfor more accurately predicting how likely users are to interact withcontent items. In particular, a user interaction data, such aspast/historical data regarding whether users interacts with contentitems, user attributes of the users, and content attributes of thecontent items are stored within a data structure used to train a modelfor generating predictions as to whether users will interact withcontent items. The user attributes, the content attributes, and userinteraction indicator values indicative of whether users interacted withcertain content items are stored within fields of the data structure,such as stored within columns of a table.

In order to take into account continuity information and groupinginformation, sub-fields (e.g., additional columns) are created forcertain attributes, such as a set of sub-fields for an age attribute anda set of sub-fields for a time attribute. Each value of an attribute isrepresented by a gray code comprising digits that are each stored withina field of a set of sub-fields for the attribute. For example, 7 digitgray codes are used to represent age values from 0 to 120 for an ageattribute (e.g., age 16 may be represented as 1000010; age 20 may berepresented by 10100000, age 21 may be represented by 10110000, etc.).So, instead of age being represented by a single age value, there arenow 7 additional digits that can represent 7 different options(sub-categories) for the attribute. Each digit represents an option, anda value of the digit indicates whether an attribute value corresponds tothat option. For example, a 0 value for a gray code digit may indicatethat an age value of 30 does not corresponds to an option (e.g., is notwithin the sub-category) and a 1 value for the gray code digit mayindicate that the age value of 30 does correspond to the option (e.g.,is not part of the sub-category) or vice versa, and thus the age valueof 30 can correspond to or not 7 options (sub-categories) for the ageattribute because there are now 7 additional digits of gray codeinformation for that age value of 30. In this way, gray code is used torepresent an attribute value of an attribute using a sequences of 0 sand 1 s per digit of the gray code, where a digit corresponds to anoption (sub-category) of the attribute, and a 0 indicates that theattribute value corresponds to the option (sub-category) and the 1indicates that the attribute value does not correspond to the option(sub-category) or vice versa.

Each digit of the gray code may be correspond to a column, and thus 7additional columns may be inserted into the table for the age attribute.Each column represents a particular option (sub-category) of the ageattribute. In this way, categorical columns are added into the table todirectly represent continuity of attribute values, and thus thecategorical columns are embedded/encoded into the model (e.g., into thefactorization machine during training of the model) so that the modelcan more accurately predict affinity scores corresponding to how likelyusers will interact with content items. Because consecutive numberswithin the gray code may merely differ by a single digit (e.g., age 20represented by 10100000 may differ from age 21 represented by 10110000by merely a single digit such as the 4^(th) digit from the left),neighboring attribute values (e.g., ages 19 through 22) will have verysimilar digit values and thus belong to similar options(sub-categories). Groupings of similar attribute values and continuityof attribute values can be represented within the data structure andencoded into the model for improved predictions of how likely users areto interact with content items because of the additional considerationsof grouping information and continuity information can be used by themodel after training. This is because the model is now trained onbehaviors that are shared among groups of attribute values and trainedon behaviors that are continuous amongst multiple attribute values.

An embodiment of content recommendation based upon continuity andgrouping information of attributes is illustrated by an example method400 of FIG. 4 , and is described in conjunction with FIGS. 5 and 6 . Acontent recommendation system may be implemented by hardware, software,and/or a combination thereof, such as by a server, a plurality ofservers, a virtual machine, etc. Various computing devices, such as acontent provider of a website, an application executing on a clientdevice, etc., may access the content recommendation system over acomputer network in order to obtain content items or recommendations ofcontent items to provide to users, such as by displaying content itemsthrough a user interface, embedding content items into emails,displaying content items applications, websites, etc. The contentrecommendation system may utilize machine learning functionality and/ora model to determine what content items to provide to a requestor, suchas what content item to transmit over the network to a client device fordisplay on the client device.

In order to generate and/or train the model utilizing machine learningfunctionality, such as a factorization machine, user interaction data ofpast user interactions with content items may be used. According, userinteraction data 606, of FIG. 6 , specifying whether users interactedwith content items is obtained, at 402. The user interaction data 606may specify user attributes of the users and/or content attributes ofthe content items. The user attributes may comprise demographicinformation of users, locations of users (e.g., a home location, a worklocation, a current location, etc.), browsing history of users,interests of users (e.g., social network groups joined by users, topicsof emails, messages, social network posts, forums, and/or othercontent/activities of the users), age, gender, etc. The contentattributes may comprise information about content items, such as anidentifier, a topic/category (e.g., food, travel, sports, etc.), a mediatype, and/or other attributes of content items.

At 404, a data structure 602 may be populated with the user interactiondata 606, as illustrated by FIG. 6 . User attribute values of userattribute 608 may be populated within sets of fields within the datastructure 602. For example, age values of an age attribute 620 may bepopulated within a first set of fields (e.g., an age column within atable). Gender values of a gender attribute 622 may be populated withina second set of fields (e.g., a gender column within the table). Anynumber of user attributes 608 may be represented within the datastructure 602. The data structure 602 may be populated with contentattribute values of content attributes 610, such as within sets offields within the data structure 602. For example, content identifiervalues of a content identifier attribute 616 may be populated within athird set of fields (e.g., a content identifier column within thetable). Content category values of a category content attribute 618 maybe populated within a fourth set of fields (e.g., a category contentcolumn within the table). User interaction indicator values 614 of userinteraction indicator data 612 of whether users interacted with contentitems or did not (e.g., did a user click on a link to a website topurchase a service, did a user purchase the service, did the user view asuggested movie, did the user listen to a suggested song, etc.) may bepopulated within a fourth set of fields (e.g., an interaction column ofthe table).

The machine learning functionality and/or the model are natively definedto expect input of the data structure 602 having finite sets of discretevalues for attributes. Thus, the model is restricted to merely makingpredictions based upon individuals discrete attribute values/categorieswithout the ability to leverage continuity and grouping information suchas where users from ages 20 to 22 might behave similarly or users maybehave similarly at 2:00am through 4:00am, which would otherwise lead tomore precise and accurate predictions as to whether users would interactwith content items. Accordingly, as provided herein, the data structure602 is modified and encoded with continuity information and groupinginformation inserted 624 as sub-fields into the data structure 602 togenerate a modified data structure 604 used to train and encode themodel with the continuity information and the grouping information touse when predicting whether users will interact with content items.

At 406, the data structure 602 is modified by inserting 624 thesub-fields for attributes, such as user attributes, into the datastructure 602 to create the modified data structure 604. A set ofsub-fields for an attribute are an encoding of continuity informationand grouping information representing options (sub-categories) for theattribute, and a sub-field of the set of sub-fields for an attributevalue is populated with a value corresponding an option for theattribute. The value may indicate whether a user having the attributevalue (or content item for a sub-field for a content attribute)corresponds to the option (sub-category) or not (e.g., whether the useris part of a sub-category or not). The continuity informationcorresponds to an attribute with non-discrete values (e.g., each digitwithin each sub-field has a value indicating whether an attribute ispart of a corresponding option (sub-category) as opposed to theattribute being represented by a single value, thus allowing forgroupings of attribute values having similar correspondence to the sameoptions/categories indicative of grouping information and continuityinformation) even though the machine learning functionality isconfigured to process discrete values instead of non-discrete values.

In an example, the set of sub-fields for an attribute are inserted intothe modified data structure 604 as set of columns for the attribute. Theset of sub-fields for the attribute may be populated with gray codeinformation corresponding to the options/categories (sub-categories) forthe attribute. For example, a set of columns (sub-fields) may beinserted into the modified data structure 604 for the age attribute 620,and are populated with gray code information corresponding tooptions/categories (sub-categories) for the age attribute 620. A column640 may correspond to the already existing age value information, suchas 20 years old for a first entry of a first female user, 21 years oldfor a second entry of a first male user, 16 years old for a third entryfor a second male user, etc. A first column 63 8 may be inserted intothe modified data structure 604 as a first set of fields within which afirst digit of gray code information can be stored. The first digitcorresponds to a first option (sub-category) for the age attribute 620.A value of the first digit, such as a 0 or 1, indicates whether an ageattribute value of the age attribute corresponds to the first option(sub-category) of the age attribute, such as whether a 20 year old ageattribute value corresponds to the first option, whether a 21 year oldage attribute value corresponds to the first option, whether a 16 yearold age attribute value corresponds to the first option, etc. Forexample, the 20, 21, and 16 year old age attribute values specify avalue of 1 for the first digit in the first column 638, and thus the 20,21, and 16 year old age attribute values may correspond to the firstoption.

A second column 636 may be inserted into the modified data structure 604as a second set of fields within which a second digit of gray codeinformation can be stored. The second digit corresponds to a secondoption (sub-category) for the age attribute 620. A value of the seconddigit, such as a 0 or 1, indicates whether an age attribute value of theage attribute corresponds to the second option (sub-category) of the ageattribute, such as whether a 20 year old age attribute value correspondsto the second option, whether a 21 year old age attribute valuecorresponds to the second option, whether a 16 year old age attributevalue corresponds to the second option, etc. For example, the 20, 21,and 16 year old age attribute values specify a value of 0 for the seconddigit in the second column 636, and thus the 20, 21, and 16 year old ageattribute values may not correspond to the second option.

A third column 634 may be inserted into the modified data structure 604as a third set of fields within which a third digit of gray codeinformation can be stored. The third digit corresponds to a third option(sub-category) for the age attribute 620. A value of the third digit,such as a 0 or 1, indicates whether an age attribute value of the ageattribute corresponds to the third option (sub-category) of the ageattribute, such as whether a 20 year old age attribute value correspondsto the third option, whether a 21 year old age attribute valuecorresponds to the third option, whether a 16 year old age attributevalue corresponds to the third option, etc. For example, the 20 and 21year old age attribute values specify a value of 1 for the third digitwithin the third column 634, and thus the 20 and 21 year old ageattributes may correspond to the third option. The 16 year old ageattribute value specifies a value of 0 for the third digit within thethird column 634, and thus the 16 year old age attribute value may notcorrespond to the third option.

A fourth column 632 may be inserted into the modified data structure 604as a fourth set of fields within which a fourth digit of gray codeinformation can be stored. The fourth digit corresponds to a fourthoption (sub-category) for the age attribute 620. A value of the fourthdigit, such as a 0 or 1, indicates whether an age attribute value of theage attribute corresponds to the fourth option (sub-category) of the ageattribute, such as whether a 20 year old age attribute value correspondsto the fourth option, whether a 21 year old age attribute valuecorresponds to the fourth option, whether a 16 year old age attributevalue corresponds to the fourth option, etc. For example, the 20 and 16year old age attribute values specify a value of 0 for the fourth digitwithin the fourth column 632, and thus the 20 and 16 year old ageattributes may not correspond to the fourth option. The 21 year old ageattribute value specifies a value of 1 for the fourth digit within thefourth column 632, and thus the 21 year old age attribute value maycorrespond to the fourth option.

A fifth column 630 may be inserted into the modified data structure 604as a fifth set of fields within which a fifth digit of gray codeinformation can be stored. The fifth digit corresponds to a fifth option(sub-category) for the age attribute 620. A value of the fifth digit,such as a 0 or 1, indicates whether an age attribute value of the ageattribute corresponds to the fifth option (sub-category) of the ageattribute, such as whether a 20 year old age attribute value correspondsto the fifth option, whether a 21 year old age attribute valuecorresponds to the fifth option, whether a 16 year old age attributevalue corresponds to the fifth option, etc. For example, the 20, 21, and16 year old age attribute values specify a value of 0 for the fifthdigit within the fifth column 630, and thus the 20, 21, and 16 year oldage attributes may not correspond to the fifth option.

A sixth column 628 may be inserted into the modified data structure 604as a sixth set of fields within which a sixth digit of gray codeinformation can be stored. The sixth digit corresponds to a sixth option(sub-category) for the age attribute 620. A value of the sixth digit,such as a 0 or 1, indicates whether an age attribute value of the ageattribute corresponds to the sixth option (sub-category) of the ageattribute, such as whether a 20 year old age attribute value correspondsto the sixth option, whether a 21 year old age attribute valuecorresponds to the sixth option, whether a 16 year old age attributevalue corresponds to the sixth option, etc. For example, the 20 and 21year old age attribute values specify a value of 0 for the sixth digitwithin the sixth column 628, and thus the 20 and 21 year old ageattributes may not correspond to the sixth option. The 16 year old ageattribute value specifies a value of 1 for the sixth digit within thesixth column 628, and thus the 16 year old age attribute value maycorrespond to the sixth option.

A seventh column 626 may be inserted into the modified data structure604 as a seventh set of fields within which a seventh digit of gray codeinformation can be stored. The seventh digit corresponds to a seventhoption (sub-category) for the age attribute 620. A value of the seventhdigit, such as a 0 or 1, indicates whether an age attribute value of theage attribute corresponds to the seventh option (sub-category) of theage attribute, such as whether a 20 year old age attribute valuecorresponds to the seventh option, whether a 21 year old age attributevalue corresponds to the seventh option, whether a 16 year old ageattribute value corresponds to the seventh option, etc. For example, the20, 21, and 16 year old age attribute values specify a value of 0 forthe seventh digit within the seventh column 626, and thus the 20, 21,and 16 year old age attribute values may not correspond to the seventhoption.

In this way, the 7 digits of gray code information can be inserted intothe modified data structure 604 to encode continuity information and/orgrouping information. This is because each consecutive gray code may bedifferent by merely a single digit. For example, the gray code of1010000 for the 20 year old age attribute value differs from the graycode of 1011000 for the 21 year old age attribute value by merely thefourth digit. Thus, the 20 year old age attribute value and the 21 yearold age attribute value share the first, second, third, fifth, sixth,and seventh option (sub-category), and may be grouped together in orderto consider continuity of those options (sub-categories) shared betweenthe otherwise two discrete age attribute values of 20 and 21. When themodel is trained using the modified data structure 604, the model isencoded to take into account this continuity and grouping informationindicating that certain age attribute values, such as 20 and 21, mayexhibit similar user behavior of the first, second, third, fifth, sixth,and seventh options (sub-categories) in common between them.

In an embodiment, the continuity information and the groupinginformation may be encoded utilizing a monotonic gray code, where eachconsecutive gray code value differs in only 1 digit and the last graycode value can be radically different than the first gray code value. Inan embodiment, the continuity information and the grouping informationmay be encoded utilizing a cyclic gray code. For example, a cycle graycode of 3 digits may be where a first attribute value of an attribute isassign a first gray code of 000, a second attribute value of theattribute is assigned a second gray code of 001, a third attribute valueof the attribute is assigned a third gray code of 011, a fourthattribute value of the attribute is assigned a fourth gray code of 010,a fifth attribute value of the attribute is assigned a fifth gray codevalue of 111, a sixth attribute value of the attribute is assigned asixth gray code value of 101, and a seventh attribute value of theattribute is assigned a seventh gray code value of 100. This gray codeinformation is cyclic because each consecutive gray code value differsin only 1 digit, and the last gray code value 100 only differ from thefirst gray code value 000 by a single digit.

FIG. 5 illustrates an example of a visual representation of a cyclicgray code 500 that may be utilized to encode continuity information andgrouping information into a model used to predict likelihoods of usersinteracting with content items. The visual representation of the cyclicgray code 500 represents 16 different gray codes that can be used torepresent 16 different options (sub-categories) for an attribute, suchas a user attribute, a content attribute, a numerical attribute, etc. Afirst gray code 0000 is assigned to a first attribute value of theattribute (e.g., an age attribute value of 1 years old). A second graycode 0001 is assigned to a second attribute value of the attribute(e.g., an age attribute value of 2 years old). A third gray code 0011 isassigned to a third attribute value of the attribute (e.g., an ageattribute value of 3 years old). A fourth gray code 0011 is assigned toa fourth attribute value of the attribute (e.g., an age attribute valueof 4 years old). A fifth gray code 0110 is assigned to a fifth attributevalue of the attribute (e.g., an age attribute value of 5 years old). Inthis way, 16 different gray codes can be assigned to 16 attribute valuesof the attribute.

Each gray code has 4 digits. Each digit of a gray code may correspond toan option (sub-category) of the attribute (e.g., sub-categories of anage attribute). A value of a digit may indicate whether an attributevalue represented by the gray code corresponds to the option(sub-category) or not. In this way, an attribute value can be encodedwith continuity information and grouping information relating to 4different options (sub-categories) that can be used to group relatedattribute values (e.g., because the first gray code of 0000 and thesecond gray code of 0001 differ only by 1 digit, the first attributevalue and the second attribute value may be similar with respect to 3 ofthe 4 options, and thus can be grouped together as being similar).Related attribute values can be grouped to convey continuity information(e.g., 1 year olds and 2 year olds may behave similarly, and thus thecontinuity of age from and between being 1 years old and 2 years old canbe encoded into the model).

The first digit of each gray code in visual representation of the cyclicgray code 500 corresponds to an inner circle 512, where white boxesrepresent 0 and black boxes represent 1 for the first digit. The seconddigit of each gray code in the visual representation of the cyclic graycode 500 corresponds to a circle 514. The third digit of each gray codein the visual representation of the cyclic gray code 500 corresponds toa circle 516. The fourth digit of each gray code in the visualrepresentation of the cyclic gray code 500 corresponds to an outercircle 518.

Once the modified data structure 604 is created, the modified datastructure 604 is processed using machine learning functionality togenerate the model, at 408. Because the additional sub-fields of graycode information are inserted into the modified data structure 604, themodel is trained and encoded with continuity information (e.g., insteadof separately understanding and considering discrete time values, themodel can understand a continuous flow of time where a time period cancorrespond to similar user behavior, such as where users behavesimilarly from 1:00am to 4:00am, which can be understand by the modelbecause gray codes for those time attribute values of 1:00am to 4:00ammay correspond to similar options/sub-categories of a time attribute)and grouping information (e.g., users that are 20 years old to 24 yearsold may behave similarly, which can be understood by the model becausegray codes for those age attribute values of 20 to 24 may correspond tosimilar options/sub-categories of an age attribute) in order to improvethe ability of the model to predict likelihoods of whether users willinteract with content. In this way, the model is trained on datapopulated within the modified data structure 604.

After the model is generated and trained, the model may be utilized forselectively identifying and/or providing content items to users, such asfor display through a user interface, an audio message played by acomputing device, an image populated within a webpage, an email message,a text message, a push notification, etc. In an example, a firstcomputing device configured to host a website may determine that aclient computing device is requesting a webpage of the website. Thefirst computing device may transmit a request over a network to a secondcomputing device hosting the content recommendation system. The contentrecommendation system may identify user attributes of a user associatedwithin the client computing device (e.g., the user attributes may beprovided by the first computing device or may be maintained by thesecond computing device).

The content recommendation system utilizes the user attributes of theuser and content attributes of available content items to provide to theuser as input to the model. The model generations predictions, such asscores, so to how likely the user is to interact with each content item.In this way, the model ranks the content items based upon how likely theuser is to interact with each content item. A content item have aparticular rank (e.g., a rank above a threshold, a highest rank, etc.)may be transmitted by the content recommendation system from the secondcomputing device to the first computing device over a network. In thisway, the content item can be populated and displayed through thewebpage. In another example, the content item may be directly providedto the client computing device.

FIG. 7 illustrates an example of a system 700 for training a model 706to output predictions of how likely users are to interact with contentitems. Machine learning functionality 704, such as a factorizationmachine, may be configured to train the model 706 using user interactiondata 702 corresponding to historic/past user interactions with contentitems. Generally, the machine learning functionality 704 may understandand expect the user interaction data 702 to specify attributes as finitesets of discrete values, and thus the machine learning functionality 704is unable to train the model 706 to account for continuity of attributevalues (e.g., the model 706 can only evaluate and take into account asingle attribute value in isolation, such as a single hour as opposed tothe concept of time being continuous as opposed to 24 individualisolated hours) and grouping of similar attributes (e.g., users withsimilar ages, such as users between 50 and 53 years old, may behavesimilarly).

Accordingly, as provided herein, the user interaction data 702 isencoded with continuity information and grouping information byassigning gray code values for each attribute value of an attribute(e.g., a gray code value of 000 may be assigned to 12:00am, a gray codevalue of 001 may be assigned to 1:00am, etc.). Each digit of a gray codevalue can correspond to an option (sub-category) of the attribute. Inthis way, additional information (e.g., whether an attribute valuecorresponds to an option or not) can be encoded into the userinteraction data for training the model 706 to take into accountcontinuity information and grouping information. Accordingly, themachine learning functionality 704 trains the model 706 based upon theuser interaction action data 702 that is augmented with gray codeinformation, such as cyclic gray code values or monotonic gray codevalues.

FIG. 8 illustrates an example of a system 800 for providing contentitems that are selected utilizing a model 806 trained on continuityinformation and grouping information derived from gray codes assigned toattribute values of user interaction data used to train the model 806.The model 806 may be maintained by a computing device that hosts acontent recommendation system. The content recommendation system mayreceive a request or indication of an opportunity to provide a user of aclient device 810 with a content item. Content attributes of contentitems 802 and user attributes 804 of the user are input into the model806. The model 806 evaluates the content attributes and the userattributes 804 to generate predictions 808 of how likely the user is tointeract with each of the content items 802 (e.g., a prediction maycomprise a value, such as from 0 to 5, of how likely the user willinteract with a content item). The content recommendation system maytransmit a content item 812, such as a recommendation to try item Chaving a highest prediction, to the client device 810 for display to theuser.

FIG. 9 illustrates an example of a system 900 for providing contentitems that are selected utilizing a model 906 trained on continuityinformation and grouping information derived from gray codes assigned toattribute values of user interaction data used to train the model 906.The model 906 may be maintained by a computing device that hosts a moviestreaming service. The content recommendation system may determine thata client device 910 has accessed the movie streaming service, such aswhere a user launches an application configured to play movies from themovie streaming service. Content attributes of content items 902 (e.g.,attributes of movies available from the movie streaming service, such aslength, topic, release date, etc.) and user attributes 904 of the userare input into the model 906. The model 906 evaluates the contentattributes and the user attributes 904 to generate predictions 908 ofhow likely the user is to watch each movie (e.g., a prediction maycomprise a value, such as from 0 to 5, of how likely the user will watcha movie). The content recommendation system may transmit arecommendation 912 of a particular movie (e.g., movie A with a highestprediction) to the client device 910 for display to the user.

FIG. 10 is an illustration of a scenario 1000 involving an examplenon-transitory machine readable medium 1002. The non-transitory machinereadable medium 1002 may comprise processor-executable instructions 1012that when executed by a processor 1016 cause performance (e.g., by theprocessor 1016) of at least some of the provisions herein. Thenon-transitory machine readable medium 1002 may comprise a memorysemiconductor (e.g., a semiconductor utilizing static random accessmemory (SRAM), dynamic random access memory (DRAM), and/or synchronousdynamic random access memory (SDRAM) technologies), a platter of a harddisk drive, a flash memory device, or a magnetic or optical disc (suchas a compact disk (CD), a digital versatile disk (DVD), or floppy disk).The example non-transitory machine readable medium 1002 storescomputer-readable data 1004 that, when subjected to reading 1006 by areader 1010 of a device 1008 (e.g., a read head of a hard disk drive, ora read operation invoked on a solid-state storage device), express theprocessor-executable instructions 1012. In some embodiments, theprocessor-executable instructions 1012, when executed cause performanceof operations, such as at least some of the example method 400 of FIG. 4, for example. In some embodiments, the processor-executableinstructions 1012 are configured to cause implementation of a system,such as at least some of the example system 600 of FIG. 6 , at leastsome of the example system 700 of FIG. 7 , at least some of the examplesystem 800 of FIG. 8 , and/or at least some of the example system 900 ofFIG. 9 , for example.

3. Usage of Terms

As used in this application, “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Unless specified otherwise, “first,” “second,” and/or the like are notintended to imply a temporal aspect, a spatial aspect, an ordering, etc.Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first object and a secondobject generally correspond to object A and object B or two different ortwo identical objects or the same object.

Moreover, “example” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused herein, “or” is intended to mean an inclusive “or” rather than anexclusive “or”. In addition, “a” and “an” as used in this applicationare generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form. Also,at least one of A and B and/or the like generally means A or B or both Aand B. Furthermore, to the extent that “includes”, “having”, “has”,“with”, and/or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising”.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Various operations of embodiments are provided herein. In an embodiment,one or more of the operations described may constitute computer readableinstructions stored on one or more computer readable media, which ifexecuted by a computing device, will cause the computing device toperform the operations described. The order in which some or all of theoperations are described should not be construed as to imply that theseoperations are necessarily order dependent. Alternative ordering will beappreciated by one skilled in the art having the benefit of thisdescription. Further, it will be understood that not all operations arenecessarily present in each embodiment provided herein. Also, it will beunderstood that not all operations are necessary in some embodiments.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method, comprising: executing, on a processorof a computing device, instructions that cause the computing device toperform operations, the operations comprising: modifying a datastructure by inserting a plurality of sub-fields for a user attribute,wherein a sub-field is populated with a value representing an option ofthe user attribute and the plurality of sub-fields are an encoding ofcontinuity information and grouping information representing options forthe user attribute; processing the data structure using machine learningfunctionality to generate a model; and utilizing the model to generate aprediction as to whether a user will interact with a content item. 2.The method of claim 1, comprising: populating the plurality ofsub-fields with gray code information corresponding to the options forthe user attribute.
 3. The method of claim 1, wherein the data structurecomprises a table and user attribute values of the user attribute arepopulated within a first set of fields within a first column of thetable.
 4. The method of claim 3, wherein content attribute values of acontent attribute are populated within a second set of fields within asecond column of the table.
 5. The method of claim 4, wherein userinteraction indicator values are populated within a third set of fieldswithin a third column of the table.
 6. The method of claim 3, whereinthe plurality of sub-fields correspond to a set of columns inserted intothe table for the user attribute.
 7. The method of claim 1, comprising:utilizing the model to rank two or more content items based upon alikelihood the user will interact with the two or more content items. 8.The method of claim 7, comprising: transmitting a content item, having arank above a threshold, over a network to a computer for display to theuser.
 9. The method of claim 1, comprising: populating the plurality ofsub-fields with cyclic gray code information corresponding to theoptions for the user attribute.
 10. The method of claim 1, comprising:populating the plurality of sub-fields with monotonic gray codeinformation corresponding to the options for the user attribute.
 11. Themethod of claim 1, comprising: populating the plurality of sub-fieldswith gray code values corresponding to the options for the userattribute, wherein consecutive gray code values differ by a singledigit.
 12. The method of claim 1, wherein the machine learningfunctionality comprises a factorization machine.
 13. A computing devicecomprising: a processor; and memory comprising processor-executableinstructions that when executed by the processor cause performance ofoperations, the operations comprising: modifying a data structure byinserting a plurality of sub-fields for a user attribute, wherein asub-field is populated with a value representing an option of the userattribute and the plurality of sub-fields are an encoding of continuityinformation and grouping information representing options for the userattribute; processing the data structure using machine learningfunctionality to generate a model; and utilizing the model to generate aprediction as to whether a user will interact with a content item. 14.The computing device of claim 13, wherein the operations comprise:populating the plurality of sub-fields with gray code informationcorresponding to the options for the user attribute.
 15. The computingdevice of claim 13, wherein the data structure comprises a table anduser attribute values of the user attribute are populated within a firstset of fields within a first column of the table.
 16. The computingdevice of claim 15, wherein content attribute values of a contentattribute are populated within a second set of fields within a secondcolumn of the table, and wherein user interaction indicator values arepopulated within a third set of fields within a third column of thetable.
 17. The computing device of claim 13, wherein the continuityinformation corresponds to an attribute having non-discrete values andthe machine learning functionality is configured to process discretevalues and not non-discrete values.
 18. The computing device of claim15, wherein the plurality of sub-fields correspond to a set of columnsinserted into the table for the user attribute.
 19. A non-transitorymachine readable medium having stored thereon processor-executableinstructions that when executed cause performance of operations, theoperations comprising: modifying a data structure by inserting aplurality of sub-fields for an attribute, wherein the plurality ofsub-fields are an encoding with gray code information and groupinginformation corresponding to options for the attribute, wherein theattribute comprises at least one of a user attribute or a contentattribute; processing the data structure using machine learningfunctionality to generate a model; and utilizing the model to generate aprediction as to whether a user will interact with a content item. 20.The non-transitory machine readable medium of claim 19, wherein theoperations comprise: utilizing the model to rank two or more contentitems based upon a likelihood the user will interact with the two ormore content items; and transmitting a content item, having a rank abovea threshold, over a network to a computer for display to the user.